University of Oulu

Tavakolian M., Hadid A. (2018) Deep Discriminative Model for Video Classification. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11208. Springer, Cham

Deep discriminative model for video classification

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Author: Tavakolian, Mohammad1; Hadid, Abdenour1
Organizations: 1Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, )
Persistent link: http://urn.fi/urn:isbn:9783030012250
Language: English
Published: Springer Nature, 2018
Publish Date: 2019-06-06
Description:

Abstract

This paper presents a new deep learning approach for video-based scene classification. We design a Heterogeneous Deep Discriminative Model (HDDM) whose parameters are initialized by performing an unsupervised pre-training in a layer-wise fashion using Gaussian Restricted Boltzmann Machines (GRBM). In order to avoid the redundancy of adjacent frames, we extract spatiotemporal variation patterns within frames and represent them sparsely using Sparse Cubic Symmetrical Pattern (SCSP). Then, a pre-initialized HDDM is separately trained using the videos of each class to learn class-specific models. According to the minimum reconstruction error from the learnt class-specific models, a weighted voting strategy is employed for the classification. The performance of the proposed method is extensively evaluated on two action recognition datasets; UCF101 and Hollywood II, and three dynamic texture and dynamic scene datasets; DynTex, YUPENN, and Maryland. The experimental results and comparisons against state-of-the-art methods demonstrate that the proposed method consistently achieves superior performance on all datasets.

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Series: Lecture notes in computer science
ISSN: 0302-9743
ISSN-E: 1611-3349
ISSN-L: 0302-9743
ISBN: 978-3-030-01225-0
ISBN Print: 978-3-030-01224-3
Pages: 401 - 418
DOI: 10.1007/978-3-030-01225-0_24
OADOI: https://oadoi.org/10.1007/978-3-030-01225-0_24
Host publication: ECCV 2018: Computer Vision – ECCV 2018
Host publication editor: Ferrari, Vittorio
Hebert, Martial
Sminchisescu, Cristian
Weiss, Yair
Conference: European conference on computer vision
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: The financial support of the Academy of Finland and Infotech Oulu is acknowledged.
Copyright information: © Springer Nature Switzerland AG 2018. This is a post-peer-review, pre-copyedit version of an article published in ECCV 2018: Computer Vision – ECCV 2018. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-01225-0_24.